Neurofuzzy approaches for predicting financial time series are investigated and shown to perform well in the context of various trading strategies involving stocks and options. The horizon of prediction is typically a few days and trading strategies are examined using historical data. Two methodologies are presented wherein neural predictors are used to anticipate the general behavior of financ...

Stock price prediction has attracted much attention from both practitioners and researchers. However, most studies in this area ignored the non-stationary nature of stock price series. That is, stock price series do not exhibit identical statistical properties at each point of time. As a result, the relationships between stock price series and their predictors are quite dynamic. It is challengi...

This paper uses Þnancial structure data via internet network to enable the prediction of Þnancial distress. Financial distress is deÞned as a Þrm that has entered into liquidation, receivership or is declared as of negligible value by the Inland Revenue. A sample of distressed and nondistressed quoted companies listed on the London Stock Exchange for the last 20 years (1980 1999) was obtained f...

Using sentiment information in the analysis of financial markets has attracted much attention. Natural language processing methods can be used to extract market from texts such as news articles. The objective this paper is articles and use estimated scores predict price direction stock index Standard & Poor’s 500. To achieve best possible performance classification, state-of-the-art bidirec...